• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.125-125
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• 1991

This paper presents a framework for the hierarchical PPC(Production Planning and Control) in make-to-order environment with job shop. The characteristics of the environment are described as : 1) project with non-repetitive and individual production, 2) short delivery date, 3) process layout with large scales manufacturing. 4) job shops. The PPC in a make-to-order typically are organized along hierarchical fashions. A model is proposed for the hierarchical job shop scheduling based on new concepts of production system, work and worker organization. Then, a new integrated hierarchical framework is also developed for the PPC based on concepts of the proposed job shops scheduling model. Finally, the proposed framework has been implemented in the Electric Motor Manufacturing and the results showed good performance.

• Korean Management Science Review
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• v.16 no.2
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• pp.125-135
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• 1999

This paper presents a framework for the hierarchical PPC(Production Planning and Control) in make-to-order environment with job shop. The characteristics of the environment are described as : 1) project with non-repetitive and individual production, 2) short delivery date, 3) process layout with large scales manufacturing. 4) job shops. The PPC in a make-to-order typically are organized along hierarchical fashions. A model is proposed for the hierarchical job shop scheduling based on new concepts of production system, work and worker organization. Then, a new integrated hierarchical framework is also developed for the PPC based on concepts of the proposed job shops scheduling model. Finally, the proposed framework has been implemented in the Electric Motor Manufacturing and the results showed good performance.

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.1
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• pp.41-49
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• 2010

In this paper, we propose a hierarchical RAM simulation model framework which are used to analyze the RAM specifications on the concept refinement phase. The hierarchical RAM simulation model framework consists of RAM simulation models, class library and each model's input and output data lists. The hierarchical RAM simulation models are co-operated with 3 kinds of model - type I, II, III. Type I, II models are used to analyze the target operational availability and Type III is used to establish the initial RAM specifications. Each model's input and output data lists are defined by considering each model's purpose of RAM analysis. The class library is arranged with each model's classes for implementing the hierarchical simulation models. The proposed framework may be applied for executing the RAM activities effectively.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.176-176
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• 2018

This study presents a regional, probabilistic framework for estimating streamflow via spatial scaling in the Great Lakes basin, which is the largest lake system in the world. The framework follows a two-fold strategy including (1) a quadratic-programming based optimization model a priori to explore the model structure, and (2) a time-varying hierarchical Bayesian model based on insights found in the optimization model. The proposed model is developed to explore three innovations in hierarchical modeling for reconstructing historical streamflow at ungaged sites: (1) information of physical characteristics is utilized in spatial scaling, (2) a time-varying approach is introduced based on climate information, and (3) heteroscedasticity in residual errors is considered to improve streamflow predictive distributions. The proposed model is developed and calibrated in a hierarchical Bayesian framework to pool regional information across sites and enhance regionalization skill. The model is validated in a cross-validation framework along with four simpler nested formulations and the optimization model to confirm specific hypotheses embedded in the full model structure. The nested models assume a similar hierarchical Bayesian structure to our proposed model with their own set of simplifications and omissions. Results suggest that each of three innovations improve historical out-of-sample streamflow reconstructions although these improvements vary corrsponding to each innovation. Finally, we conclude with a discussion of possible model improvements considered by additional model structure and covariates.

• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.436-441
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• 2015

There are many types of clustering algorithms such as centroid, hierarchical, or density-based methods. Each algorithm has unique data grouping principles, which creates different varieties of clusters. Ordering Points To Identify the Clustering Structure (OPTICS) is a well-known density-based algorithm to analyze arbitrary shaped and varying density clusters, but the obtained clusters only correlate loosely. Hierarchical agglomerative clustering (HAC) reveals a hierarchical structure of clusters, but is unable to clearly find non-convex shaped clusters. In this paper, we provide a novel hierarchy generation framework and application which can aid users by combining the advantages of the two clustering methods.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.433-434
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• 2006

A new disparity estimation method in hierarchical frameworks is proposed. The two main ideas for improving accuracy are to obtain an object boundary map for distinction of homogeneous/object boundary region and to choose adaptive window size/shapes. Moreover, for the reduction of computational complexity, we change reference regions in hierarchical framework. The experimental results show that the proposed method can acquire good results which are robust to homogeneous and object boundary regions.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1429-1440
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• 2008

In a meta-analysis combining the results from different clinical trials, it is important to consider the possible heterogeneity in outcomes between trials. Such variations can be regarded as random effects. Thus, random-effect models such as HGLMs (hierarchical generalized linear models) are very useful. In this paper, we propose a HGLM framework for analyzing the binominal response data which may have variations in the odds-ratios between clinical trials. We also present the prediction intervals for random effects which are in practice useful to investigate the heterogeneity of the trial effects. The proposed method is illustrated with a real-data set on 22 trials about respiratory tract infections. We further demonstrate that an appropriate HGLM can be confirmed via model-selection criteria.

• Journal of Information Processing Systems
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• v.15 no.1
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• pp.67-90
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• 2019

Accurate detection, tracking and analysis of human movement using robots and other visual surveillance systems is still a challenge. Efforts are on to make the system robust against constraints such as variation in shape, size, pose and occlusion. Traditional methods of detection used the sliding window approach which involved scanning of various sizes of windows across an image. This paper concentrates on employing a state-of-the-art, hierarchical graph based method for segmentation. It has two stages: part level segmentation for color-consistent segments and object level segmentation for category-consistent regions. The tracking phase is achieved by employing SIFT keypoint descriptor based technique in a combined matching and tracking scheme with validation phase. Localization of human region in each frame is performed by keypoints by casting votes for the center of the human detected region. As it is difficult to avoid incorrect keypoints, a consensus-based framework is used to detect voting behavior. The designed methodology is tested on the video sequences having 3 to 4 persons.

• Smart Structures and Systems
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• v.31 no.4
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• pp.325-334
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• 2023

Computer vision-based damage detection enables non-contact, efficient and low-cost bridge health monitoring, which reduces the need for labor-intensive manual inspection or that for a large number of on-site sensing instruments. By leveraging recent semantic segmentation approaches, we can detect regions of critical structural components and identify damages at pixel level on images. However, existing methods perform poorly when detecting small and thin damages (e.g., cracks); the problem is exacerbated by imbalanced samples. To this end, we incorporate domain knowledge to introduce a hierarchical semantic segmentation framework that imposes a hierarchical semantic relationship between component categories and damage types. For instance, certain types of concrete cracks are only present on bridge columns, and therefore the noncolumn region may be masked out when detecting such damages. In this way, the damage detection model focuses on extracting features from relevant structural components and avoid those from irrelevant regions. We also utilize multi-scale augmentation to preserve contextual information of each image, without losing the ability to handle small and/or thin damages. In addition, our framework employs an importance sampling, where images with rare components are sampled more often, to address sample imbalance. We evaluated our framework on a public synthetic dataset that consists of 2,000 railway bridges. Our framework achieves a 0.836 mean intersection over union (IoU) for structural component segmentation and a 0.483 mean IoU for damage segmentation. Our results have in total 5% and 18% improvements for the structural component segmentation and damage segmentation tasks, respectively, compared to the best-performing baseline model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.11
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• pp.4644-4661
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• 2015

Wireless sensor networks are often organized in the form of clusters leading to the new framework of WSN called cluster or hierarchical WSN where each cluster head is responsible for its own cluster and its members. These hierarchical WSN are prone to various routing layer attacks such as Black hole, Gray hole, Sybil, Wormhole, Flooding etc. These routing layer attacks try to spoof, falsify or drop the packets during the packet routing process. They may even flood the network with unwanted data packets. If one cluster head is captured and made malicious, the entire cluster member nodes beneath the cluster get affected. On the other hand if the cluster member nodes are malicious, due to the broadcast wireless communication between all the source nodes it can disrupt the entire cluster functions. Thereby a scheme which can detect both the malicious cluster member and cluster head is the current need. Abnormal energy consumption of nodes is used to identify the malicious activity. To serve this purpose a learning based energy prediction algorithm is proposed. Thus a two level energy prediction based intrusion detection scheme to detect the malicious cluster head and cluster member is proposed and simulations were carried out using NS2-Mannasim framework. Simulation results achieved good detection ratio and less false positive.